Wealth inequality: data and models Marco Cagetti and Mariacristina De Nardi
Fe
dera
l Res
erve
Ban
k of
Chi
cago
WP 2005-10
Wealth inequality: data and models∗
Marco CagettiUniversity of Virginia
Mariacristina De NardiFederal Reserve Bank of Chicago and University of Minnesota
August 17, 2005
Abstract
In the United States wealth is highly concentrated and very un-equally distributed: the richest 1% hold one third of the total wealth inthe economy. Understanding the determinants of wealth inequality isa challenge for many economic models. We summarize some key factsabout the wealth distribution and what economic models have been ableto explain so far.
∗We gratefully acknowledge financial support from NSF grants (respectively) SES-0318014 and SES-0317872. We are grateful to Marco Bassetto, Wojciech Kopczuk andan anonymous referee for helpful comments. The views expressed herein are those of the au-thors and not necessarily those of the Federal Reserve Bank of Chicago, the Federal ReserveSystem, or the NSF.
1 Introduction
In the United States wealth is highly concentrated and very unequally dis-
tributed: the richest 1% of the households own one third of the total wealth in
the economy. Understanding the determinants of wealth inequality is a chal-
lenge for many economic models. In this paper, we summarize what is known
about the wealth distribution and what economic models have been able to
explain so far.
The development of various data sets in the past 30 years (in particular
the Survey of Consumer Finances) has allowed economists to quantify more
precisely the degree of wealth concentration in the United States. The picture
that emerged from the different waves of these surveys confirmed the fact that
a large fraction of the total wealth in the economy is concentrated in the hand
of the richest percentiles: the top 1% hold one third, and the richest 5% hold
more than half of total wealth. At the other extreme, a significant fraction of
the population holds little or no wealth at all.
Income is also unequally distributed, and a large body of work has studied
earnings and wage inequality. Income inequality leads to wealth inequality as
well, but income is much less concentrated than wealth, and economic mod-
els have had difficulties in quantitatively generating the observed degree of
wealth concentration from the observed income inequality. The question is
what mechanisms are necessary to generate saving behavior that leads to a
distribution of asset holdings consistent with the actual data.
In this work, we describe the main framework for studying wealth in-
equality, that of general equilibrium models with heterogeneous agents, in
which some elements of a life-cycle structure and of intergenerational links are
present. Some models consider a dynasty as a single, infinitely-lived agent,
1
while others consider more explicitly the life-cycle aspect of the saving deci-
sion. Baseline versions of these models are unable to replicate the observed
wealth concentration. More recently, however, some works have shown that
certain ingredients are necessary, and sometimes enable the model to replicate
the data. Bequests are a key determinants of inequality, and careful mod-
elling of bequests is vital to understand wealth concentration. In addition,
entrepreneurs constitute a large fraction of the very rich, and models that ex-
plicitly consider the entrepreneurial saving decision succeed in dramatically
increasing wealth dispersion. The type of earnings risk faced by the richest is
also a potential explanation worth investigating.
Considerable work must still be done to better understand the quantitative
importance of each factor in determining wealth inequality and to understand
which models are most useful and computationally convenient to study it. The
recent advances in modelling have however already helped in providing a more
precise picture. The challenge now is improve these models even further and
to apply them to the study of several problems for which inequality is a key
determinant. For instance, the effects of several tax policies (in particular the
estate tax) might depend crucially on how wealth is concentrated in the hands
of the richest percentiles of the distribution. In the last section of this paper,
we highlight some of the areas in which models of inequality could and should
be profitably employed and extended.
2 Data
We first summarize the main facts about the wealth distribution in the United
States, facts provided mainly by the Survey of Consumer Finances. We also
mention some facts about the historical trends, although in this paper we do
2
not focus on understanding them (an area on which little work has been done
so far).
2.1 Data sources
The main source of microeconomic data on wealth for the United States is
the Survey of Consumer Finances (SCF)1 which, starting from 1983, every
three years collects detailed information about wealth for a cross-section of
households. It also includes a limited panel (between 1983 and 1989), as well as
a link to two previous smaller surveys (1962 Survey of Financial Characteristics
of Consumers and the 1963 Survey of Changes in Family Finances).
The SCF was explicitly designed to measure the balance sheet of house-
holds and the distribution of wealth. It has a large number of detailed questions
about different assets and liabilities, which allows highly disaggregated data
analysis on each component of the total net worth of the household. More
importantly, the SCF oversamples rich households by including, in addition to
a national area probability sample (representing the entire population), a list
sample drawn from tax records (to extract a list of high income households).
Oversampling is especially important given the high degree of wealth concen-
tration observed in the data (see Davies and Shorrocks [31]). For this reason,
the SCF is able to provide a more accurate measure of wealth inequality and
of total wealth holdings: Curtin et al. [29] and Antoniewicz [6] document that
the total net worth implied by the SCF matches quite well the total wealth
implied by the (aggregate) Flow of Funds Accounts (although not perfectly,
especially when disaggregating the various components).
1The survey is publicly available from the Federal Reserve Board website athttp://www.federalreserve.gov/pubs/oss/oss2/scfindex.html.
3
Unfortunately, the SCF does not follow households over time, unlike the
Panel Study of Income Dynamics (PSID). The PSID2 is a longitudinal study,
which begun in 1968, and follows families and individuals over time. It focuses
on income and demographic variables, but since 1984 it has also included (every
5 years) a supplement with questions on wealth. The PSID includes a national
sample of low-income families, but it does not oversample the rich. As a result,
this data set is unable to describe appropriately the right tail of the wealth
distribution: Curtin et al. [29] show that the PSID tracks the distribution of
total household net worth implied by the SCF only up to the top 2%-3% of
richest household, but misses much of the wealth holdings of the top richest.
Given that the richest 5% hold more than half of the total net worth in the
United States, this is an important shortcoming.
Another important data source is the Health and Retirement Study (HRS),
which recently absorbed the Study of Assets and Health Dynamics Among the
Oldest Old (AHEAD). This survey focuses on the older households (from before
retirement and on), and provides a large amount of information regarding their
economic and health condition. However, as the PSID, this survey misses the
richest households.
Other data sets also contain some information on wealth and asset holdings
(for instance, the U.S. Bureau of Census’s Survey of Income and Program Par-
ticipation, or, for the very richest, the data on the richest 400 people identified
by the Forbes magazine and, indirectly, the Internal Revenue Service data on
estate returns). However, because of its careful sample choice, the SCF re-
mains the main source of information about the distribution of wealth in the
United States. Due to their demographic and health data, the PSID and the
2See http://psidonline.isr.umich.edu/.
4
Percentile Yeargroup 1989 1992 1995 1998 20010-49.9 2.7 3.3 3.6 3.0 2.850-89.9 29.9 29.7 28.6 28.4 27.490-94.9 13.0 12.6 11.9 11.4 12.195-98.9 24.1 24.4 21.3 23.3 25.099-100 30.3 30.2 34.6 33.9 32.7
Table 1: Percent of net worth held by various groups defined in terms ofpercentiles of the wealth distribution (taken from Kennickell [58], p. 9).
HRS provide additional information for studying the wealth holdings of most
households (except the richest).
2.2 Wealth concentration in the United States
The most striking aspect of the wealth distribution in the United States is
its degree of concentration. Table 1 shows that the households in the top 1%
of the wealth distribution hold around one third of the total wealth in the
economy, and those in the top 5% hold more than half. At the other extreme,
many households (more than 10%) have little or no assets at all.
The data in Table 1 and 2 refer to total net worth. There are many possible
measures of wealth, the most appropriate one depending on the problem ob-
ject of study. Net worth includes all assets held by the households (real estate,
financial wealth, vehicles) net of all liabilities (mortgages and other debts); it
is thus a comprehensive measure of most marketable wealth. This measure
thus includes the value of most defined contribution plans (such as IRAs), but
excludes the implied values of defined benefit plans and social security. Defined
contribution plans can of course be important sources of income after retire-
5
Net Yearworth 1989 1992 1995 1998 2001< $0 7.3 7.2 7.1 8.0 6.9$0-$1,000 8.0 6.3 5.2 5.8 5.4$1,000-$5,000 12.7 14.4 15.0 13.1 12.8$25,000-$100,000 23.2 25.4 26.4 22.9 22.0$100,000-$250,000 20.2 21.6 22.1 22.6 19.2$250,000-$500,000 11.0 9.3 9.3 12.0 13.0$500,000-$1,000,000 5.4 4.6 5.1 6.0 7.8≥ $1,000,000 4.7 3.8 3.6 4.9 7.0
Table 2: Percent distribution of household net worth over wealth groups, 2001dollars(taken from Kennickell [58], p. 9).
ment; but their measure is problematic because their value has to be imputed.
To study other questions it may be useful to look at more restricted measures
of wealth, that for example exclude less liquid assets (such as housing), and
focus on financial wealth instead. Throughout this paper, we focus on net
worth.3
The key facts about the distribution of wealth have been highlighted in a
large number of studies, among others in Wolff [96], [94], and Kennickell [58].
Wealth is extremely concentrated, and much more so than earnings and in-
come, as shown by Dıaz-Gimenez et al. [34] and Budria et al. [84]. For instance,
in 1992 the Gini index for labor earnings, income (inclusive of transfers) and
wealth were respectively .63, .57, and .78 (Dıaz-Gimenez et al. [34]), while in
1995 they were .61, .55 and .80 (Budria et al. [84]). These two studies also
3It must be noted that the exact definition of net worth varies across studies. Therefore,the numbers we cite below when referring to other works are not directly comparable, asthey may include different sets of assets. However, the general picture of a highly skeweddistribution and the main trends are unchanged and do not depend on the exact measureof wealth.
6
Top % 1 5 10 20Whole populationpercentage of total net worth held 30 54 67 81Entrepreneurspercentage of households in a given percentile 63 49 39 28percentage of net worth held in a given percentile 68 58 53 47
Table 3: Entrepreneurs and the distribution of wealth. SCF 1989.
report that the correlation between these three variables is positive, but far
from perfect.
There is also significant wealth inequality within various age and demo-
graphic groups. For instance, Venti and Wise [91] and Bernheim at al. [12]
show that wealth is highly dispersed at retirement even for people with similar
lifetime incomes, and argue that this differences cannot be explained only by
events such as family status, health and inheritances, nor by portfolio choice.
Several studies have also highlighted the differences in wealth holdings
across different groups. There are large gaps in wealth holdings by race (see
for example Altonji and Doraszelski [3] and Smith [88]). Wolff [96] documents
that in the 1980s and 1990s the ratio of average net worth of blacks to the
one of whites was around 18%. Unfortunately little work has been done to
quantitatively understand the sources of this persistent difference across race
groups. (See White [92] for a study of how much of current black-white income
and wealth inequality can be explained by initial conditions at Emancipation.)
There is also a large difference in wealth holdings between entrepreneurs
and non-entrepreneurs, as shown in Table 3 (taken from Cagetti and De
Nardi [20]). Entrepreneurs4 are a small fraction of the population (about
4We classify as entrepreneurs the households who declare owning a privately held business
7
10%), but hold a large share of total wealth (about 40%). Table 3 shows that
entrepreneurs constitute a large fraction of the richest households: more than
60% of the households in the top 1%, and almost one-half of those in the top
5%, and they hold, respectively, 68% and 58% of the wealth held by households
in those percentiles. As shown also by Gentry and Hubbard [41], Quadrini [81],
and Buera [17], entrepreneurship is a key element to understand the wealth
concentration among the richest households.
Regarding household mobility, Hurst et al. [56] use PSID data to ana-
lyze the wealth dynamics between 1984 and 1994, for different socio-economic
groups and for different types of asset holdings, pointing out that most of
the mobility occurs in the midrange deciles, while the top and bottom ones
show high persistence. Unfortunately, the PSID does not allow to study what
happens at the top percentile. Using the same dataset, Quadrini [81] studies
the wealth mobility for entrepreneurs and non-entrepreneurs, showing that en-
trepreneurs are more upwardly mobile. Because of the purely cross-sectional
nature of the SCF, it is difficult to characterize the mobility of households
across the wealth distribution.
The key feature that we stress here is that the observed degree of wealth
concentration is much higher than the one of labor earnings and that, as we
will see in the sections about the models, generating saving behavior that is
consistent with these facts is not a trivial task.
(or a share of one), who have an active management role in it, and who have invested apositive amount of wealth in such business. Alternative classifications give very similarresults.
8
2.3 Savings, bequests and wealth accumulation
In addition to income differences, wealth inequality may be driven by differ-
ences in the saving behavior, or in the intergenerational transfers received.
Analyzing the empirical evidence to tell apart different potential sources of
wealth inequality is challenging.
Individual saving cannot be measured directly but must be computed from
other data, either as the first difference in wealth, or as income minus consump-
tion. For this reason there are fewer studies that document the heterogeneity
in saving rates; their findings suggest significant differences in saving behavior
across various groups. (See Browning and Lusardi [16] for a review of the lit-
erature.) Dynan et al. [35] show that higher-lifetime income households save
a larger fraction of their income than lower-income households. Quadrini [81]
documents that entrepreneurs, who tend to be among the richest households,
also exhibit higher saving rates.
Bequests also play an important role in shaping wealth inequality. Kotlikoff
and Summers [60] were the first to argue that life-cycle savings for retirement
account for a small fraction of total capital accumulation, while intergenera-
tional transmission of wealth accounts for the vast majority of capital forma-
tion (with a baseline estimate of around 80% of the total). Further studies have
confirmed the importance of intergenerational transfers; for instance, Gale and
Scholz [40] find that bequests account for about 30% of total wealth accumu-
lation, and intended inter-vivos transfers account for an additional 20%.
It is more difficult to measure the size of intended bequests relative to that
of purely accidental ones, due to uncertainty about the life-span. Hurd [54]
estimates a very low marginal utility from leaving bequests. Altonji and Vil-
lanueva [4] also find relatively small values for the elasticity of bequests to
9
permanent income, although they do show that this number increases with
life-time resources. Most of the bequests, however, are concentrated among
the top wealth percentiles, a group that these papers ignore. Looking at a
sample of TIAA-CREF retirees (whose average wealth is higher than in the
other groups), Laitner and Juster [65] find that about half of the households in
their sample plan to leave estate and that the amount of wealth attributable
to estate building is significant, accounting for half or more of the total for
those who plan to leave bequests. While more empirical research is needed in
the area, it appears that intergenerational altruism and intended bequests are
a crucial element to understand the distribution of wealth, above all for the
very rich.
2.4 Wealth inequality outside the United States
While we focus on the United States, it is interesting to compare its wealth
distribution to that of other countries. The evidence on a few other, mostly
OECD countries is summarized in Wolff [95] and in Davies and Shorrocks [31],
to which we refer for the data and a discussion (including the caveats about
data quality for some countries). In all countries for which studies exist, wealth
is very unequally distributed, with Gini indexes ranging from .50 to .80, and
a wealth share for the top 5% of households ranging from around 25% to over
50%. Among these countries, the United States exhibits the highest degree of
wealth concentration, with the largest shares of total wealth in the hand of the
richest percentiles of the wealth distribution. The lowest values are found in,
among others, Australia, Italy, Japan and Sweden, and intermediate values in
Canada, France and the United Kingdom.
These data indicate that, while the main forces generating wealth inequal-
10
ity seem to be common across developed countries, certain factors may differ
across countries and reduce, or increase, inequality. Among these factors, pub-
lic policies such as taxation (estate taxation and progressive income taxation in
particular) tend to reduce wealth accumulation among the richest households,
and hence inequality. More research needs to be done to explain cross-country
differences (see De Nardi [32] as an application that tries to understand the
various determinants of inequality both in the United States and Sweden).
2.5 Trends in wealth inequality
It is difficult to measure wealth inequality before the second half of the twen-
tieth century. For the United States some data exist (Census surveys in the
nineteenth century and other records of estates), but their interpretation is still
debated. Some argue that inequality has always been high and has changed
little from the end of the eighteenth century to the first decades of the twenti-
eth century (for example, Soltow [89]), while others argue for a sharp increase
in inequality over the period (among others, Lindert [68]). It is however inter-
esting to note that wealth inequality has always been substantial, and, even
according to Lindert [68], by 1860 the richest 1% held approximately 30% of
total wealth, an amount that remained more or less stable until the 1920’s.
There is evidence that U.S. wealth inequality decreased significantly be-
tween the 1920 and the 1970s (Davies and Shorrocks [31], Wolff and Mar-
ley [97], and Kopczuk and Saez [59]). Wolff [94], for instance, documents that
the share of total wealth held by the top 1% of individuals fell from 38% in
1922 to 19% in 1976. As explained by Kopczuk and Saez [59], the decrease
took place between the onset of the Great Depression and the end of World
War II, and was most likely generated by the Depression and the New Deal
11
policies that increased the tax burden for the wealthy. Given the continuing
high estate and income taxes, the top shares did not recover in the decades
after World War II. Inequality increased again in the 1980s. Wolff [93] argues
that while wealth inequality fell during the 1970s, it rose sharply after 1979,
with a dramatic increase over the 1980s, to the level off in the 1990s. The
trend in the 1990s is much less clear. The decade saw a stockmarket boom
and the rise of some large internet fortunes, as well as increased income con-
centration (Piketty and Saez [78]). While Wolff [96] suggests a small increase
in wealth inequality over the decade, Kennickell [58], Kopczuk and Saez [59],
and Scholz [86] find that the share of total wealth in the hands of the richest
remained stable in the 1990s.
Some of these trends have also been observed in other countries (see Davies
and Shorrocks [31], Piketty et al. [77], and Saez [85]). As in the United States,
wealth inequality decreased in the U.K., Sweden and France during the first
half of the twentieth century. This decrease was especially strong in the U.K.,
where wealth inequality in previous centuries was at least as high, if not higher,
than in the United States. However in these countries, and unlike in the United
States, inequality kept decreasing significantly after World War II and until
the 1980s. In contrast, the United States experienced an increase in inequality
after the 1970s. Different levels of income and estate taxation, which in the
last 50 years have been higher in these countries, might be responsible for the
difference in these dynamics.
3 Models
In the following sections we describe the models used so far to study wealth con-
centration. Most of these models are general-equilibrium, quantitative models
12
with heterogeneous agents. For expositional purposes we classify these works
into three sub-categories: models with infinitely-lived dynasties, models with
overlapping-generations (OLG), and models that mix both of these features5.
The first type of models ignore the life-cycle structure, but consider each
dynasty as a single agent who lives forever. The second type explicitly intro-
duces an age and life-cycle structure, with various degrees of intergenerational
transmission of wealth and abilities. The third type relaxes the infinitely-
lived dynasty assumption of the first type of models, but greatly simplifies the
life-cycle structure.
Almost all the current general equilibrium, quantitative models of wealth
inequality are versions of Bewley models6. These are incomplete-markets mod-
els in which households are ex-ante identical7, in the sense that they face the
same stochastic labor earnings and ability processes, but are ex-post heteroge-
neous because they receive different realizations of such shocks. These models
are typically solved for stationary equilibria in which, over time, there is a
constant distribution of people over the relevant state variables for the econ-
omy, but people move around in the distribution, and thus face considerable
uncertainty. This type of framework endogenously generate differences in asset
holdings, and hence wealth concentration, as a result of the household’s desire
to save and the realization of the shocks. An exogenous earnings process is
typically the source of these shocks, and its properties are usually estimated
5As we further explain later on, this categorization does not always reflect the chronolog-ical order of the various contributions. Historically, the starting point of this literature wasModigliani and Brumberg’s [74] life-cycle model with certainty, which was then enriched invarious ways by several contributors.
6See Ljungqvist and Sargent [70] for an exposition of the properties of these models andof their numerical solution.
7See Quadrini and Rıos-Rull [83] for a discussion about why we need incomplete marketmodels to study wealth inequality.
13
using micro-level data sets.
4 Dynasty models
4.1 A general framework
Let us consider the simplest version of a Bewley model with infinitely-lived
agents. There is a continuum of agents. All agents have identical preferences,
and have the following utility function when they first enter the model econ-
omy:
E
{ ∞∑t=1
βtu(ct)
},
where u(ct) is the constant relative-risk aversion flow of utility from consump-
tion. The labor endowment of each household is given by an idiosyncratic
labor productivity shock z that assumes a finite number of possible values and
follows a first order Markov process with transition matrix (Γ(z)). There is
only one asset, a, that people can use to self-insure against earnings risk.
A constant returns to scale production technology converts aggregate cap-
ital (K) and aggregate labor (L) into aggregate output (Y ).
During each period each household chooses how much to consume (c) and
save for next period by holding risk free assets (a′). The household’s state
variables are denoted by x = (a, z), where a is asset holdings carried into the
period and z is the labor shock endowment.
The household’s recursive problem can thus be written as
V (x) = max(c,a′)
{u(c) + βE
[V (a′, z′)|x
]}
14
subject to
c + a′ = (1 + r)a + zw
c ≥ 0, a′ ≥ a
where r is the interest rate net of taxes and depreciation, w is the wage, and
a is a net borrowing limit8. For simplicity, we have not explicitly introduce
taxes and government policies, but of course the setup can easily accomodate
various types of taxes and transfers.
At every point in time this model economy can be described by a probability
distribution of people over assets a and earnings shocks z.
A stationary equilibrium for this economy is a set of consumption and
saving rules, prices, aggregate capital and labor, and invariant distribution of
households over the state variables of the system such that:
1. Given prices, the decision rules solve the household’s recursive problem
described above.
2. Aggregate capital is equal to total savings of all of the households of the
economy, while aggregate labor is equal to total labor supplied by all of
the households of the economy.
3. Prices, that is the interest rate and the wage rate, gross of taxes, equal
the marginal product of capital, net of depreciation, and the marginal
product of labor.
4. The constant distribution of people is the one induced by the law of
motion of the system, which is determined by the exogenous earnings
8See Bewley [14], Aiyagari [1], Huggett [52] and Ljungqvist and Sargent [70] for moreexhausting descriptions of this framework and its equilibrium.
15
shocks and by the endogenous policy functions of the households.
Quadrini and Rıos-Rull [83] nicely summarize the results obtained from
this type of models until 1997 with the first three lines of Table 4.
% wealth in topGini 1% 5% 20%U.S. data.78 29 53 80Baseline Aiyagari.38 3.2 12.2 41.0High variability Aiyagari.41 4.0 15.6 44.6Quadrini: entrepreneurs.74 24.9 45.8 73.2
Table 4: Dynasty models of wealth inequality.
Most of the models in Table 4 display significantly less wealth concentration
than in the data. The reason why households save in this type of models is to
create a buffer stock of assets to self-insure against earnings fluctuations (as
discussed by Carroll [24]). Once such buffer stock is reached, the agents don’t
save any more, and the model is thus not capable to explain the creation of
large fortunes.
Differences in wealth holdings are generated by random earnings shocks.
Households hit by a series of negative shocks use part of this buffer stock of
wealth, and thus become poorer, relative to those who have had a series of
positive shocks. Larger earnings shocks may thus generate more dispersion.
Lines two and three of the table compare two identical economies, except for
the fact that the second one displays much higher earnings variability than
the first one (and thus higher cross-sectional earnings inequality) and show
16
that second economy does generate a slightly more concentrated distribution
of wealth.
The table shows however that earnings shocks alone cannot generate a
level of wealth inequality comparable to that found in the data9. The reason
is that there is no mechanism that induces the richer people to keep saving at
high rates. Indeed, the model implies that richer households, having reached
high levels of wealth, should save at lower rates (or even dissave) than poorer
households, who instead need to rebuild their buffer stock. This implication is
in contrast with the empirical studies on saving rates (see for example Dynan
et al. [35] and Quadrini [81]).
Allowing for heterogeneity in households’ earnings processes may generate
different levels of buffer stocks of wealth across people. This effect is quantita-
tively not strong enough to generate a significant increase in wealth inequality
when the earnings volatility used is consistent with those estimated in micro
studies (see Carroll [24] and Quadrini and Rıos-Rull [83]). Regarding het-
erogeneity in earnings across educational groups, various estimates (see for
example Cunha et al. [28]) show that earnings variability decreases with edu-
cation. This implies, everything else equal, that people with lower education
have higher saving rates. If one were to use these estimated differences in
earnings shocks in a model with different educational groups, one would thus
find (everything else equal) less wealth dispersion than in the case in which
the same volatility for all groups is assumed.
9This observation assumes that ones uses micro-level data set to estimate the level of theindividual’s earnings volatility. As discussed in section 6, much higher variability may givedifferent results.
17
4.2 Extensions of the basic model
The failure of the basic model to explain wealth inequality suggests that one
needs to look at other mechanisms. Two such mechanisms are entrepreneurship
and preference heterogeneity.
The setup presented so far assumes implicitly that the agents are employed
workers, who receive some labor income. Entrepreneurs, however, face a dif-
ferent decision problem, as their income is related to their business. A more
recent contribution by Quadrini [82] introduces entrepreneurial choice in a dy-
nastic framework: during each period the households decide whether to be
entrepreneurs or not. Quadrini finds that a calibrated version of his model
can generate a much larger amount of wealth concentration in the hands of
the richest. In his model, three elements are crucial to generate this result.
First, the existence of capital market imperfections induces workers that have
entrepreneurial ideas to accumulate more wealth to reach minimal capital re-
quirements. Second, in the presence of costly financial intermediation, the
interest rate on borrowing is higher than the return from saving, therefore an
entrepreneur whose net worth is negative faces a higher marginal return from
saving and reducing his debt. Third, there is additional risk associated with
being an entrepreneur, hence risk averse individuals will save more. Quadrini
chooses some of the parameters of his model to match moments of the dis-
tribution of wealth and he comes much closer to fitting the upper tail of the
wealth distribution than the previous models, although his model still does
not generate enough asset holdings in the hands of the very richest compared
to the data.
Another mechanism that has been used to generate wealth inequality is
heterogeneity in preferences. The decision to save depends crucially on the
18
specific parameter values of the utility function. In particular, a higher degree
of patience (summarized by a higher discount factor β) leads people to save
more. In the presence of precautionary savings, a higher coefficient of risk
aversion may also induce higher savings.
Krusell and Smith [61] generalize the basic framework by adding a stochas-
tic process for the dynasty’s preferences (both discount factor and risk aver-
sion). The discount factor (or the risk aversion) changes on average every
generation and is meant to recover the fact that parents and children in the
same dynasty may have different preferences. Krusell and Smith find that it
is possible to find a stochastic process for the dynasties’ discount factor to
match the variance of the cross-sectional distribution of wealth, while uncer-
tainty about risk aversion does not affect the results much (although, as shown
by Cagetti [18], the results are very sensitive to the values for the utility pa-
rameters chosen). While capturing the variance, their model fails to match the
extreme degree of concentration of wealth in the hands of the richest 1%.
There is empirical evidence of heterogeneity in preferences, in particular in
the discount factor (as show for instance by Lawrance [66] and Cagetti [19]),
and this may play an important role in shaping wealth inequality. Given
Krusell and Smith’s results, however, preference heterogeneity alone does not
seem sufficient to replicate the observed wealth concentration.
One possibility is to enrich the functional form for the utility function.
Dıaz, Pijoan-Mas, and Rıos-Rull [33] study the effect of habit formation in
preferences and find that introducing habit formation decreases the concen-
tration of wealth generated by this type of models and is hence not helpful in
reconciling the models with the key features of wealth concentration.
Yet another possibility is to assume directly that wealth per se enters the
utility function. Carroll [25] concentrates on the fact that in the data house-
19
holds with higher levels of lifetime income have higher lifetime saving rates
(see Dynan, Skinner and Zeldes [35] and Lillard and Karoly [67]). He shows
that neither standard life-cycle, nor dynastic models can recover the saving
behavior of rich and poor families at the same time. To solve this puzzle he
suggests a “capitalist spirit” model, in which finitely lived consumers have
wealth in the utility function. This can be calibrated to make wealth a luxury
good, thus rendering nonhomothetic preferences.
5 Overlapping-generations models
5.1 Life-cycle models
The models described in the previous section ignore the life-cycle dimensions of
the saving decision and only roughly approximate intergenerational linkages.
The life-cycle theory of consumption was first developed by Modigliani
and Brumberg [74]. In their framework a household chooses consumption
by maximizing the discounted utility from consumption over a lifespan of T
years, subject to a lifetime resource constraint. In its simplest form, the utility
function for a household is given by U(c1, . . . , cT ), and the resources available
to the household are given by
N∑s=1
ys
(1 + r)s=
T∑s=1
cs
(1 + r)s,
where N denotes the number of working years. This theory has implications
for wealth inequality: households save while working, reach a maximum wealth
level at retirement age, and then decumulate their savings after retirement.
The life-cycle component of wealth inequality, however, is too small to ex-
20
plain the observed wealth concentration in the data. Atkinson [7] first showed
that even allowing for earnings growth and uncertain life-spans the model can-
not generate the wealth concentration measured in the richest decile. Wolf-
son [98] argued that the inclusion of further factors such as differences in earn-
ings, rates of return and family formation still cannot replicate the empirical
concentration.
A crucial element not incorporated in these models is the intergenerational
transmission of wealth. Becker and Tomes [10] were the first to model explicitly
the parental decision problem, by assuming that parents are altruistic and
thus value transfers to their offspring. They characterize the structure of such
transfers across generations, in the form of both human capital and bequests,
and show that in the presence of constraints, parental transfers are first in the
form of human capital, and only after the optimal amount of human capital
has been reached they do take the form of monetary transfers such as bequests.
Bequests are thus a luxury good in this framework.
The impact of bequests was soon shown to be potentially relevant. Among
the earlier, partial equilibrium exercises, Davies [30] studies the effects of vari-
ous factors, including bequests, on economic inequality in a one-period model
without uncertainty. In his setup one generation of parents care about their
children’s future consumption, and there is regression to the mean between
parents and children’s earnings. As a consequence, the income elasticity of
bequests is high and inherited wealth is a major cause of wealth inequality.
The stage was thus set to build and study quantitative models with a life-
cycle structure and overlapping generation, which was accomplished by Lait-
ner [63], who developed a model with two sided altruism among generations,
constraints on net worth being non negative, and random lifetime earnings
(abstracting thus from life-span uncertainty and earnings shocks). In this
21
setup intergenerational transfers are a luxury good and liquidity constraints
are less binding for generations receiving larger transfers. This economy can
also generate realistic capital to output ratios, but Laitner did not explore the
implications of his model for wealth inequality.
We now turn to the models that build on this earlier literature and we
discuss their quantitative findings.
5.2 A benchmark OLG framework
We use Huggett’s [52] formulation as a benchmark OLG. Each period a con-
tinuum of agents are born. They live at most N periods, and face an age-
dependent survival probability st of surviving up to age t, conditional on sur-
viving up to age t − 1. The demographic patterns are stable, so age t agents
make up a constant fraction µt of the population at every point in time.
All agents have identical preferences, and have the following utility function
when they first enter the model economy:
E
{N∑
t=1
βt(Πt
j=1st
)u(ct)
},
where u(ct) is the constant relative-risk aversion flow of utility from consump-
tion, and the expected value is computed with respect to the household’s earn-
ings shocks.
The labor endowment of each household is given by a function e(z, t), which
depends on the agent’s age t, and on an idiosyncratic labor productivity shock
z, that assumes a finite number of possible values and that follows a first order
Markov chain with transition matrix Γ(z).
22
There are no annuity markets10. People save to insure against earnings risk,
for retirement, and in case they live a long life. People that die prematurely
leave accidental bequests.
There is a constant returns to scale production technology that converts
aggregate capital (K) and labor (L) into output (Y ).
During each period each household choose how much to consume (c) and
save for next period by holding risk free assets (a′). The household’s state
variables are denoted by x = (a, z), where a is asset holdings carried into the
period and z is the labor shock endowment.
The household’s recursive problem can be written as:
V (x, t) = max(c,a′)
{u(c) + βst+1E
[v(a′, z′, t + 1)|x
]}
subject to
c + a′ = (1 + r)a + e(z, t)w + T + bt
c ≥ 0, a′ ≥ a and a′ ≥ 0 if t = N,
where r is the interest rate net of taxes and depreciation, w is the wage net
of taxes, T are accidental bequests that left by all of the deceased in a period,
which are assumed to be redistributed by the government to all people alive,
and bt are social security payments to the retirees. Modelling explicitly social
security is important because social security redistributes a significant fraction
of income from the young to the old and thus reduces the saving rate and
changes the aggregate capital-output ratio.
10This is a very common assumption given how small the annuity market is in practice.Eichenbaum and Peled [37] show that in the presence of moral hazard people will choose toself-insure rather than use annuity markets even if the rate of return on annuities is high.
23
At every point in time this model economy can be described by a probability
distribution of people over age t, assets a , and earnings shocks z.
A stationary equilibrium for this economy can be defined analogously to the
one described for the infinitely-lived model, with the additional requirements
that during each period total lump-sum transfers received by the households
alive equal accidental bequests left by the deceased, and the government budget
constraint balances every period.
Huggett [52] calibrates this model economy to key features of the U.S. data
and uses different versions of it to quantify how much wealth inequality can
be generated using a pure life-cycle model with labor earnings shocks and un-
certain life span. The paper succeeds in matching the U.S. Gini coefficient for
wealth, but the concentration is obtained by having too many people holding
little wealth and by not concentrating enough wealth in the upper tail of the
wealth distribution. The key reason of this failure is that in the data the rich
(people with high permanent income) have a very high saving rate, while in
the model households that have accumulated a sufficiently high buffer stock
of assets and retirement saving don’t keep saving until they reach huge levels
of wealth. Huggett finds that relaxing the household’s borrowing constraint
increases the fraction of people bunched at zero or negative wealth, but does
not increase much the asset holdings of the rich, and hence does not help in
generating a distribution of wealth closer to the observed one.
Huggett also studies the amount of wealth inequality generated by his
model at different ages and finds that, starting from age 40, the model un-
derpredicts the amount of wealth inequality by age. This point is further
studied by a recent work by Hendricks [49], who focuses on the performance
of various models on cross-sectional wealth inequality at retirement age. Hen-
dricks shows that, at retirement age, his (simplified) version of an OLG model
24
overstates wealth differences between earnings-rich and earnings-poor, while
it understates the amount of wealth inequality conditional on similar lifetime
earnings. Yang [99] uses a framework with a more realistic life cycle structure,
borrowing constraints, and voluntary and accidental bequests. She finds that
the model’s implications are quantitatively consistent with the ones observed
in the data. The model that she uses is a version of De Nardi’s [32], which we
describe next.
5.3 Adding bequest motives
De Nardi [32] introduces two types of intergenerational links in the OLG model
used by Huggett: voluntary bequests and transmission of human capital. She
models the utility from bequests as providing “warm glow” (as in Andreoni [5]).
In this framework parents and their children are linked by voluntary and ac-
cidental bequests and by the transmission of earnings ability. The households
thus save to self-insure against labor earnings shocks and life-span risk, for
retirement, and possibly to leave bequests to their children.
Compared to Huggett, there is thus an extra term in the value function of
a retired person that faces a positive probability of death:
V (a, t) = maxc,a′
{u(c) + stβEtV (a′, t + 1) + (1− st)φ(b(a′))
}(1)
where
φ(b) = φ1
(1 +
b
φ2
)1−σ
(2)
The utility from leaving bequests thus depends on two parameters: φ1, which
represents the strength of the bequest motive, and φ2, which measures the
extent to which bequests are a luxury good. These two parameters are respec-
25
tively calibrated to match Kotlikoff and Summers’s [60] data on the fraction
of capital due to intergenerational transfers, and to match one moment of the
observed distribution of bequests.
Transfer Percentage wealth in the top Percentage withwealth Wealth negative orratio Gini 1% 5% 20% 40% 60% zero wealth
U.S. data.60 .78 29 53 80 93 98 5.8–15.0
No intergenerational links, equal bequests to all.67 .67 7 27 69 90 98 17
No intergenerational links, unequal bequests to children.38 .68 7 27 69 91 99 17
One link: parent’s bequest motive.55 .74 14 37 76 95 100 19
Both links: parent’s bequest motive and productivity inheritance.60 .76 18 42 79 95 100 19
Table 5: OLG models of wealth inequality, from De Nardi [32]
Table 5 summarizes her results. The first line of the table refers to the
U.S. data. The second one to a version of Huggett’s model economy in which
there are only accidental bequests, which are redistributed equally to all peo-
ple alive every year. The third line also refers to an economy in which there
are only accidental bequests, but these are received by the children of the de-
ceased upon their parent’s death, and are thus unequally distributed. This
experiment shows that accidental bequests, even if unequally distributed, do
not generate a more unequal distribution. This is because receipt of a bequest
per se does not alter the saving behavior of the richest. This experiment also
highlights the fact that the Auerbach and Kotlikoff’s measure on intergenera-
tional transfers is sensitive to the timing of transfers: if children inherit only
26
once, when their parent dies (rather than every year as in line three), this
measure generates a fraction of wealth due to intergenerational transfers that
is much lower than the one computed by Huggett. The fourth line allows for
a voluntary bequest motive, and shows that voluntary bequests can explain
the emergence of large estates, which are often accumulated in more than one
generation, and characterize the upper tail of the wealth distribution in the
data. The fifth line allows for both voluntary bequests and transmission of
ability and shows that a human-capital link, through which children partially
inherit the productivity of their parents, generates an even more concentrated
wealth distribution. More productive parents accumulate larger estates and
leave larger bequests to their children, who, in turn, are more productive than
average in the workplace.
The presence of a bequest motive also generates lifetime saving profiles
more consistent with the data: saving for precautionary purposes (emphasized
in particular by Carroll [24]) and saving for retirement are the primary factors
for wealth accumulation at the lower tail of the distribution, while saving to
leave bequests significantly affects the shape of the upper tail. Also, with this
parameterization of the voluntary bequest motive, and consistently with the
data, the rich elderly do not decumulate their assets as fast as predicted by a
standard a OLG model.
De Nardi finds that φ2 is a large number, so bequests are a luxury good,
and that the extent to which they are a luxury good is key in generating more
concentration in the hands of the richest and producing a more realistic lifetime
savings profiles (many papers that do not find evidence in favor of a bequest
motive, such as Hurd [54] and Hendricks [49], assume that φ2 = 0.) With this
parameterization, and consistently with the data, the bequest motive to save is
much stronger for the richest households, who, even when very old, keep some
27
assets to leave to their children. The rich leave more wealth to their offspring,
who, in turn, tend to do the same. This behavior generates some large estates
that are transmitted across generations because of the voluntary bequests,
while being quantitatively consistent with the elasticity of the savings of the
elderly to permanent income that has been estimated from microeconomic data
(Altonji and Villanueva [4]).
It is clear from this table that, although modeling explicitly both of these
mechanisms does help to better explain the the savings of the richest, De
Nardi’s model is not capable of matching the wealth concentration of the rich-
est 1% of the people.
5.4 Other extensions
Heer [48] adopts a model in which richer and poorer people have different
tastes for leaving bequests. His characterization of the labor income process
(people can be employed or unemployed) does not generate enough income
inequality compared with the data and his model does not produce large wealth
concentration.
Hendricks [50] studies the effects of allowing for preference heterogeneity
in a life-cycle framework with only accidental bequests. Consistently with
Krusell and Smith [61], he finds that heterogeneity in risk aversion has only
minimal effects on saving and wealth inequality. Moreover, he shows that time
preference heterogeneity only makes a modest contribution in accounting for
high wealth observations if the heterogeneity in discount factor is chosen to
generate realistic patterns of consumption and wealth inequality as cohorts
age.
Hubbard Skinner and Zeldes [51] focus on the effects of social insurance
28
programs on wealth holdings of poorer people because micro data find a sig-
nificant group in the population with little wealth. They show that in presence
of precautionary savings the asset-based means testing of welfare programs can
imply that a significant fraction of people with lower lifetime earnings do not
accumulate wealth.
Gokhale et al. [42] aim at evaluating how much wealth inequality at re-
tirement age arises from inheritance inequality. To do so, they construct an
overlapping-generations model that allows for random death, random fertility,
assortative mating, heterogeneous human capital, progressive income taxation
and social security. All of these elements are exogenous and calibrated to the
data. The families are assumed not to care about their offspring, hence all
bequests are involuntary. To solve the model, they impose that individuals are
infinitely risk averse and that the rate of time preference equals the interest
rate. In their framework inheritances in the presence of social security play
an important role in generating intra-generational wealth inequality at retire-
ment. The intuition is that social security annuitizes completely the savings
of poor and middle-income people but is a very small fraction of the wealth of
richer people, who thus keep assets to insure against life-span risk.
6 Mixtures of life-cycle and dynastic behavior
The third class of models mixes features of both life-cycle models and infinitely-
lived dynasties, simplifying some aspects of either model to make them more
computationally tractable.
Among these works, Laitner [64] assumes that all households save for life-
cycle purposes, but only some of them care about their own descendants.
There are perfect annuity markets, therefore all bequests are voluntary, and
29
no earning risk over the life cycle, hence no precautionary savings. Laitner’s
model is simple to compute and provides a number of interesting insights.
The concentration in the upper tail of the wealth distribution is matched by
choosing the fraction of households that behave as a dynasty and also depends
on the assumptions on the distribution of wealth within the dynasty.
Nishiyama [75] adopts an OLG model with bequests and intervivos trans-
fers in which households in the same family line behave strategically. As De
Nardi, he concludes that the model with intergenerational transfers better
explains, although not fully, the observed wealth distribution.
Castaneda, Dıaz-Gimenez and Rıos–Rull [27] consider a model economy
populated by dynastic households that have some life-cycle flavor: workers
have a constant probability of retiring at each period and once they are retired
they face a constant probability of dying. Each household is perfectly altruistic
toward its household. The paper employs a number of parameters to match
some features of the U.S. data, including measures of wealth inequality.
The key feature of the model that generates huge amount of wealth holdings
in the hands of the richest is the productivity shocks process. This process is
calibrated so that the highest productivity level is more than 100 times higher
than the second highest. There thus is an enormous discrepancy between
the highest productivity level and all of the others. Moreover, if one is at
the highest productivity level, the chance of being 100 times less productive
during the next period is more than 20%. High-ability households thus face
much higher earnings risk, save at very high rates to self-insure against earnings
risk, and thus build huge buffer stocks of assets.
Building on Quadrini [81], Cagetti and De Nardi [20] take seriously the
observation that entrepreneurs, that is, households that own and manage
privately-held businesses, make up for the largest fraction of rich people in the
30
data. Cagetti and De Nardi [20] assume that households have two types of abil-
ity: entrepreneurial ability (θ), and worker’s ability (y). The entrepreneurial
ability is linked to the capacity to produce income out of capital according to
the following production function:
ye = θkν ,
where ye is income from being an entrepreneur during a period and investing
working capital k, and ν is the degree of decreasing returns to scale, or “span-
of-control,” as in Lucas [71]. The worker’s ability is linked to the ability to
earn income when working for someone else, where the worker’s income in a
period is given by:
yw = w y,
where w is the market wage.
During each period, the individual observes his abilities (which evolve
stochastically over time), and makes an occupational choice for that period.
Contracts are imperfectly enforceable: people repay the amount they own only
if it is in their own interest to do so, as in Albuquerque and Hopenhayn [2]. Be-
cause of this friction, the amount that an entrepreneur can borrow is a function
of his own wealth, which thus acts as collateral. The firm size can thus be sub-
optimal, and richer households are able to borrow more and grow faster. The
model adopts a demographic structure similar to Castaneda, Dıaz-Gimenez
and Rıos–Rull [27], and can thus incorporate the transmission of wealth, and
of businesses, across generations.
Compared to Quadrini, Cagetti and De Nardi obtain a much better fit of the
upper tail of the wealth distribution by endogenizing the firm size distribution.
31
They do not choose any of the parameter of their model to generate this result,
which should hence be interpreted as a check of the goodness of the model.
The key reason why their model succeeds in generating this large amount of
wealth concentration is linked to the fact that, while entrepreneurs could invest
capital at a higher rate of return, the presence of borrowing constraints and
collateral requirements makes the entrepreneur to save to exploit the high rate
of return even when the entrepreneur becomes “rich”. This key intuition does
not depend on the demographic structure assumed, and would also hold in a
dynastic model. Cagetti and De Nardi chose to formulate it in an economy
with more realistic life-cycle features to study the effects of government policies
such as estate taxation. The life-cycle structure makes it possible to study the
effects of government policies such as estate taxation on wealth inequality and
capital accumulation (see Cagetti and De Nardi [21]).
7 Future directions
In the previous sections, we have discussed if and to what extent the current
economic models have been able to explain the determinants of wealth inequal-
ity in the United States. While the baseline versions of the standard economic
models badly fail to replicate the degree of wealth concentration observed in
the data, some extensions have had a much greater success.
As we learn more about the determinants of wealth concentration, we can
start applying new frameworks to study many economic problems for which
inequality is a key element. In what follows, we briefly discuss some of these
areas. This discussion, of course, is by no means complete.
32
7.1 Human capital
All quantitative models of wealth inequality that we are aware of take hu-
man capital as exogenous. As documented by Huggett et al. [53], modelling
human capital investment in presence of heterogenous learning abilities and
exogenous shocks is important to reproduce the data on earnings inequal-
ity over the life cycle. Modeling human capital explicitly would also allow
a better measurement of the relative importance of human capital formation
relative to bequests in generating wealth inequality, in the spirit of Becker and
Tomes [10], especially when human capital acquisition is limited by imperfect
financial markets (as for instance in the analysis of Heckman et al. [47]). For
these reasons it would be worthwhile to study saving decisions and wealth in-
equality in a framework that also considers human capital accumulation and
disentagles the permanent and transitory sources of inequality as in Huggett
et al. [53].
7.2 Portfolio choice
The models we have discussed typically assume only one riskless asset, or at
most two when entrepreneurial investment is included. An important issue,
however, is portfolio choice. Gollier [43] provides a survey about the theory of
household portfolios, Haliassos and Michaelides [45] discuss techniques for cal-
ibrating and solving household portfolio models, while Miniaci and Weber [36]
focus on the econometric issues in the estimation of household portfolio models.
Households’ portfolio are very heterogeneous by age, income or occupation
(see for instance Bertaut and Starr-McCluer [13], Poterba and Samwick [80],
Banks et al. [9], Heaton and Lucas [46], Carroll [26], Hurd [55], and Yang [101]).
Some papers have started to study portfolio choice with heterogeneous
33
agents in a life-cycle setting. For example, Campbell et al. [22] and Campbell
and Viceira [23] have shown that the fraction of risky assets in the portfolio
should decrease with age as people move closer to retirement, and Benzoni
et al. [11] have further qualified the relation between age, income and asset
positions.
For entrepreneurial households business wealth constitutes a relevant frac-
tion of their total net worth. Heaton and Lucas [46] study the effect of business
assets on portfolio choice and asset pricing.
A large fraction of total wealth for most households is in the form of hous-
ing, a relatively illiquid and indivisible type of investment, with unique risk
and tax characteristics. While standard finance models focus on other types
of risky assets, recent works have explicitly modeled housing. For instance,
Yao and Zhang [102] show that inclusion of housing dramatically changes the
fraction of risky and riskless assets held in a portfolio, and Flavin and Ya-
mashita [39] examine the life-cycle pattern of portfolio composition induced by
lumpy housing investment. Yang [100] develops a quantitative, dynamic gen-
eral equilibrium model which, consistently with the consumption data over the
life cycle, generates both hump-shaped consumption profiles for non-housing
goods and lack of decrease of housing consumption later in life.
Poterba [79] studies the effects of taxation on portfolio choice. In the
data, households with different wealth levels hold very different portfolios (Car-
roll [26]). In order to understand the aggregate impact of portfolio decisions
and taxation on aggregate investment and equilibrium prices it is vital to
consider how wealth is distributed in the population, and in particular, to
understand the saving and portfolio of all households, including the richest
ones, who hold a disproportionate share of total wealth. The models of wealth
inequality presented in this paper may help shed light on these issues.
34
7.3 Public policy: adequacy of savings
Wealth inequality is also vital to understand policy and redistributional issues.
While wealth inequality is often seen per se as a negative aspect that must
be addressed by redistributional policies, a different question is whether the
observed levels of wealth for most households outside of the richest percentiles
are in fact in some way suboptimal and inadequate.
There has been some debate on the adequacy of savings. Some economists
believe that the wealth holding of many (or most) households are too low. Most
of these works are based on some form of myopia or inconsistency in prefer-
ences, as for instance in Lusardi [72] or in models with hyperbolic discounting
as in Laibson et al. [62]. Given this lack of foresight or commitment, house-
holds tend to save less than it is optimal, and thus government intervention
may improve welfare.
In contrast, other works have shown that the currently observed levels of
wealth are consistent with a rational, optimizing life-cycle model of wealth
accumulation. For instance, Engen et al. [38] show that the amount of sav-
ings of most households (except those at the bottom quartile of the wealth
distribution) is similar if not larger than that implied by a standard life-cycle
model of wealth accumulation with social security and retirement benefits,
while Scholz et al. [87] argue that, even for most households in the bottom of
the distribution, the wealth deficit, relative to the optimal target, is generally
small.
The key element of these works is the current level of social security bene-
fits and other transfers after retirement. While the individual decisions may be
optimal given those policies, an entirely different questions is whether the cur-
rent amount of social security is optimal, and whether aggregate welfare may
35
be improved by different schemes and different saving program incentives such
as IRAs. There is a vast literature on this topic, and the question remains, to
a large extent, unresolved. We will not try to summarize the various positions,
but we point out that careful quantitative models of wealth inequality can help
shed light on the issue.
7.4 Public policy: tax reforms
An area of public policy that crucially depend on wealth inequality is taxation,
in particular for those taxes that mainly hit the richest households, such as
estate and progressive income taxes.
Given the current exemption levels, a very small fraction of people pays
any estate tax (approximately 2% of estates are taxed), and the aggregate
revenue for the tax is a relatively small .3% of GDP. However, the households
that pay the tax are also those who do most of the saving and hold a large
fraction of total wealth. Therefore, their behavior may have a large effect on
the aggregates in the economy.
Reforms of these taxes are now being actively debated. To understand the
impact of such reforms, it is important to understand how many of these rich
households are affected, and and how strongly they are affected. Quantitative
models that carefully analyze the determinants of wealth inequality are thus
key to study the problem. Using such models, Meh [73] studies changes in
the degree of tax progressivity in Quadrini’s [82] model, and Cagetti and De
Nardi [21] study estate and income taxation in their setup.
36
7.5 Macroeconomics and the representative agent
When analyzing the effects of aggregate shocks, macroeconomics typically as-
sumes a representative agent. This allows a considerable simplification, at the
cost of ignoring the effect of heterogeneity in the population. While heterogene-
ity may be irrelevant for studying some macroeconomic problems, Browning
et al. [15] have argued that in certain cases the behavior of an economy with
many agents is significantly different from that of a representative agent one.
Few works so far have been able to address the issue. The difficulty lies in the
fact that with heterogeneity and aggregate shocks, the distribution of people
over state variables may change over time, and, at least in theory, one needs to
keep track of the distribution as an additional state variable. The Bewley-type
of models studied so far consider steady states without aggregate shocks, in
which therefore the distribution is constant. But in the presence of aggregate
shocks this is not true anymore.
Krusell and Smith [61] were the first to solve a model with aggregate pro-
ductivity shocks and heterogeneous agents. They find that in their model
heterogeneity does not matter for aggregate movements, a result partly con-
firmed also by Storesletten et al. [90], who extend their setup to a life-cycle
economy. In this economy, heterogeneity may have consequences for mobility
and individual welfare, but does not affect the aggregate movements due to
the business cycle. It is exactly because of this irrelevance that these authors
are able to solve their models numerically. When the distribution has little
effect on the aggregates, it ceases to be a significant state variable, and one
need only keep track of it its mean, or at most its variance.
While they generate wealth inequality, these models fail to replicate the
extreme degree of wealth concentration and the fraction of wealth held by the
37
richest percentiles. As argued before, the behavior of this group may be quite
different from that of the median households. Entrepreneurship, for instance,
may imply different responses of investment and savings to aggregate shocks.
Incorporating the insights of the models that study wealth concentration into
a setup with aggregate shocks is very interesting, but poses a considerable
computational challenge. It is necessary to solve the decision problem for a
large number of agents (as in a standard Bewley model), while at the same time
keeping track of the distribution of wealth (a function) as a state variable. As
computational power increases and new algorithms are developed, more and
more of these problems may start to be tackled.
7.6 Entrepreneurship, wealth and growth
In the paper, we have highlighted the role of entrepreneurs in determining
capital accumulation and wealth inequality in the United States. Unfortu-
nately, little empirical evidence exists for the role of entrepreneurs in shaping
wealth inequality in other countries. However, several studies suggests that
entrepreneurs are important also in other countries.
Among these, Banerjee and Newman [8] develop a framework to study how
initial wealth inequality shapes entrepreneurial decisions and in turn develop-
ment. In the presence of capital market imperfections, only richer households
can become entrepreneurs and create large firms. Thus, business formation
and growth depend on the wealth distribution, which is in turn dynamically
determined by entrepreneurial decisions.
The evidence from Thailand studied in Paulson and Townsend [76] shows
that financial constraint are key to determine business start-up, and wealthier
households are more likely to start a business and face less stringent con-
38
straints. Furthermore, entrepreneurship and financial deepening can account
for a significant fraction of the growth in total factor productivity in Thailand
(as found by Jeong and Townsend [57]), and their effect depends crucially on
the wealth distribution.
More data and empirical research on entrepreneurship may thus be key to
understand wealth accumulation and the implied wealth distribution not only
for developed, but also for developing countries.
39
References
[1] S. Rao Aiyagari. Uninsured idiosyncratic risk and aggregate saving. Quarterly Journalof Economics, 109(3):659–684, August 1994.
[2] Rui Albuquerque and Hugo A. Hopenhayn. Optimal dynamic lending contracts withimperfect enforceability. Review of Economic Studies, forthcoming.
[3] Joseph G. Altonji and Ulrich Doraszelski. The role of permanent income and de-mographics in black/white differences in wealth. Yale University, Economic GrowthCenter Discussion Papers, 2002.
[4] Joseph G. Altonji and Ernesto Villanueva. The marginal propensity to spend on adultchildren. NBER Working Paper 9811, July 2003.
[5] James Andreoni. Giving with impure altruism: Applications to charity and ricardianequivalence. Journal of Political Economy, 97:1447–1458, 1989.
[6] Rochelle L. Antoniewicz. A comparison of the household sector from the flow of fundsaccounts and the survey of consumer finances. Mimeo, October 2000.
[7] Anthony B. Atkinson. The distribution of wealth and the individual life-cycle. OxfordEconomic Papers, 23(2):239–254, July 1971.
[8] Abhijit V. Banerjee and Andrew F. Newman. Occupational choice and the process ofdevelopment. Journal of Political Economy, 102(2):274–298, April 1993.
[9] James Banks, Richard Blundell, and James P. Smith. Understanding differences inhousehold financial wealth between the United States and Great Britain. Journal ofHuman Resources, 38(2):241–279, Spring 2003.
[10] Gary Becker and Nigel Tomes. Human capital and the rise and fall of families. Journalof Labor Economics, 4(3):S1–S39, July 1979.
[11] Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein. Portfolio choice overthe life-cycle in the presence of ’trickle down’ labor income. Mimeo, 2004.
[12] B. Douglas Bernheim, Jonathan Skinner, and Steven Weinberg. What accounts for thevariation in retirement wealth among U.S. households? American Economic Review,91(4):832–857, September 2001.
[13] Carol Bertaut and Martha Starr-McCluer. Household portfolios in the United States.In Guiso et al. [44], pages 181–217.
[14] Truman F. Bewley. The permanent income hypothesis: A theoretical formulation.Journal of Economic Theory, 16(2):252–292, December 1977.
[15] Martin Browning, Lars Peter Hansen, and James Heckman. Micro data and generalequilibrium models. In John Taylor and Michael Woodford, editors, Handbook ofMacroeconomics, volume 1A, pages 543–633. Elsevier, 1999.
[16] Martin Browning and Annamaria Lusardi. Household saving: Micro theories andmicro facts. Journal of Economic Literature, 24:1797–1855, December 1996.
40
[17] Francisco Buera. A dynamic model of entrepreneurship with borrowing constraints.Mimeo, 2004.
[18] Marco Cagetti. Interest elasticity in a life-cycle model with precautionary savings.American Economic Review, 91(2):418–421, May 2001.
[19] Marco Cagetti. Wealth accumulation over the life cycle and precautionary savings.Journal of Business and Economic Statistics, 21(3):339–353, July 2003.
[20] Marco Cagetti and Mariacristina De Nardi. Entrepreneurship, frictions, and wealth.Federal Reserve Bank of Minneapolis Staff Report 322, September 2003.
[21] Marco Cagetti and Mariacristina De Nardi. Taxation, entrepreneurship and wealth.Federal Reserve Bank of Minneapolis Staff Report 340, July 2004.
[22] John Cambell, Joao Cocco, Francisco Gomez, and Pascal Maenhout. Investing retire-ment wealth: A life cycle model. Mimeo, March 1999.
[23] John Y. Campbell and Luis Viceira. Strategic Asset Allocation. Oxford UniversityPress, New York, NY, 2002.
[24] Christopher D. Carroll. Buffer stock saving and the life-cycle/permanent income hy-pothesis. Quarterly Journal of Economics, 112(1):1–55, February 1997.
[25] Christopher D. Carroll. Why do the rich save so much? In Joel B. Slemrod, editor,Does Atlas Shrug? The Economic Consequences of Taxing the Rich, pages 466–484.Harvard University Press, Cambridge, 2000.
[26] Christopher D. Carroll. Portfolios of the rich. In Guiso et al. [44], pages 389–430.
[27] Ana Castaneda, Javier Dıaz-Gimenez, and Jose-Victor Rıos-Rull. Accounting for theU.S. earnings and wealth inequality. Journal of Political Economy, 111(4):818–857,August 2003.
[28] Flavio Cunha, James J. Heckman, and Salvador Navarro-Lozano. Separating het-erogeneity from life-cycle earnings. Oxford Economic Papers, 57(2):191–261, April2005.
[29] Richard T. Curtin, F. Thomas Juster, and James N. Morgan. Survey estimates ofwealth: An assessment of quality. In Lipsey and Tice [69], pages 473–548.
[30] James B. Davies. The relative impact of inheritance and other factors on economicinequality. Quarterly Journal of Economics, 97(3):471–498, 1982.
[31] James B. Davies and Anthony F. Shorrocks. The distribution of wealth. In A.B.Atkinson and F. Bourguignon, editors, Handbook of Income Distribution, pages 605–675. Elsevier, 2000.
[32] Mariacristina De Nardi. Wealth inequality and intergenerational links. Review ofEconomic Studies, 71(3):743–768, July 2004.
[33] Antonia Dıaz, Josep Pijoan-Mas, and Jose-Victor Rıos-Rull. Habit formation: Impli-cations for the wealth distribution. Journal of Monetary Economics, 50(6):1257–1291,August 2002.
41
[34] Javier Dıaz-Gimenez, Vincenzo Quadrini, and Jose-Victor Rıos-Rull. Dimensions ofinequality: Facts on the U.S. distributions of earnings, income and wealth. FederalReserve Bank of Minneapolis Quarterly Review, 21(2):3–21, Spring 1997.
[35] Karen Dynan, Jonathan Skinner, and Stephen Zeldes. Do the rich save more? Journalof Political Economy, 112(2):397–444, April 2004.
[36] Raffaele Miniaci e Guglielmo Weber. Econometric issues in the estimation of householdportfolio models. In Guiso et al. [44], pages 143–178.
[37] Martin Eichenbaum and Dan Peled. Capital accumulation and annuities in an adverseselection economy. Journal of Political Economy, 95(2):334–354, April 1987.
[38] Eric M. Engen, William G. Gale, and Cori R. Uccello. The adequacy of householdsaving. Brookings Papers on Economic Activity, (2):65–187, 1999.
[39] Marjorie Flavin and Takashi Yamashita. Owner-occupied housing and the compositionof the household portfolio. American Economic Review, 92(1):345–362, 2002.
[40] William G. Gale and John Karl Scholz. Intergenerational transfers and the accumu-lation of wealth. Journal of Economic Perspectives, 8(4):145–160, Fall 1994.
[41] William M. Gentry and R. Glenn Hubbard. Entrepreneurship and household savings.Advances in Economic Analysis and Policy, 4(1), 2004. Article 1.
[42] Jagadeesh Gokhale, Laurence J. Kotlikoff, James Sefton, and Martin Weale. Simulat-ing the transmission of wealth inequality via bequests. Journal of Public Economics,79(1):93–128, 2000.
[43] Christian Gollier. What does theory have to say about household portfolios? In Guisoet al. [44], pages 27–54.
[44] Luigi Guiso, Michael Haliassos, and Tullio Jappelli, editors. Household Portfolios.MIT Press, 2002.
[45] Michael Haliassos and Alexander Michaelides. Calibration and computation of house-hold portfolio models. In Guiso et al. [44], pages 55–103.
[46] John Heaton and Deborah Lucas. Portfolio choice and asset prices: The importanceof entrepreneurial risk. Journal of Finance, 55(3):1163–1198, June 2000.
[47] James Heckman, Lance Lochner, and Cristopher Taber. Explaining rising wage in-equality: Explorations with a dynamic general equilibrium model. Review of EconomicDynamics, 1(1):1–58, 1998.
[48] Burkhard Heer. Wealth distribution and optimal inheritance taxation in life-cycleeconomies with intergenerational transfers. Mimeo. University of Cologne, Germany,1999.
[49] Lutz Hendricks. Accounting for patterns of wealth inequality. Mimeo. Iowa StateUniversity, 2004.
42
[50] Lutz Hendricks. How important is preference heterogeneity for wealth inequality?Mimeo. Iowa State University, 2004.
[51] R. Glenn Hubbard, Jonathan Skinner, and Stephen P. Zeldes. Precautionary savingand social insurance. Journal of Political Economy, 103(2):360–399, 1995.
[52] Mark Huggett. Wealth distribution in life-cycle economies. Journal of MonetaryEconomics, 38(3):469–494, December 1996.
[53] Mark Huggett, Gustavo Ventura, and Amir Yaron. Human capital and earnings dis-tribution dynamics. Mimeo, 2003.
[54] Michael D. Hurd. Mortality risk and bequests. Econometrica, 57(4):779–813, July1989.
[55] Michael D. Hurd. Portfolios of the elderly. In Guiso et al. [44], pages 431–472.
[56] Erik Hurst, Ming Ching Luoh, and Frank P. Stafford. Wealth dynamics of Americanfamilies, 1984-94. Brookings Papers on Economic Activity, (1):267–337, 1998.
[57] Hyeok Jeong and Robert M. Townsend. Sources of TFP growth: Occupational choiceand financial deepening. IEPR Working Paper Series, 2005.
[58] Arthur B. Kennickell. A rolling tide: Changes in the distribution of wealth in theU.S., 1989-2001. Mimeo, September 2003.
[59] Wojciech Kopczuk and Emmanuel Saez. Top wealth shares in the United States, 1916-2000: Evidence from estate tax returns. National Tax Journal, 57(2):445–487, June2004.
[60] Laurence J. Kotlikoff and Lawrence H. Summers. The role of intergenerational trans-fers in aggregate capital accumulation. Journal of Political Economy, 89(4):706–732,August 1981.
[61] Per Krusell and Anthony Smith, Jr. Income and wealth heterogeneity in the macroe-conomy. Journal of Political Economy, 106(5):867–896, October 1998.
[62] David I. Laibson, Andrea Repetto, and Jeremy Tobacman. Self control and retirementsavings. Brookings Papers on Economic Activity, 1(1):91–172, 1998.
[63] John Laitner. Random earnings differences, lifetime liquidity constraints, and altru-istic intergenerational transfers. Journal of Economic Theory, 58(2):135–170, 1992.
[64] John Laitner. Secular changes in wealth inequality and inheritance. The EconomicJournal, 111(474):691–721, 2001.
[65] John Laitner and Thomas F. Juster. New evidence on altruism, a study of tiaa-crefretirees. American Economic Review, 86(4):893–908, 1996.
[66] Emily Lawrance. Poverty and the rate of time preference: Evidence from panel data.Journal of Political Economy, 99:54–77, 1991.
[67] Lee A. Lillard and Lynn A. Karoly. Income and wealth accumulation over the life-cycle. Manuscript, RAND Corporation, 1997.
43
[68] Peter H. Lindert. When did inequality rise in Britain and America? Journal of IncomeDistribution, 9:11–25, 2000.
[69] Robert E. Lipsey and Helen Stone Tice, editors. The Measurement of Saving, Invest-ment and Wealth, volume 52 of Studies in Income and Wealth. University of ChicagoPress, 1989.
[70] Lars Ljungqvist and Thomas J. Sargent. Recursive Macroeconomic Theory. MITPress, Boston, MA, 2000.
[71] Robert E. Lucas, Jr. On the size distribution of business firms. The Bell Journal ofEconomics, 9(2):508–523, 1978.
[72] Annamaria Lusardi. Explaining why so many households do not save. Mimeo, January2000.
[73] Cesaire Meh. Entrepreneurship, wealth inequality, and taxation. Review of EconomicDynamics, 2005. Forthcoming.
[74] Franco Modigliani and Richard Blumberg. Utility analysis and the consumption func-tion: An interpretation of cross-sectional data. In Kenneth K. Kurihara, editor, Post-Keynesian Economics, pages 388–436. Rutgers University Press, New Brunswick, NJ,1954.
[75] Shinichi Nishiyama. Bequests, inter vivos transfers, and wealth distribution. Reviewof Economic Dynamics, 5(4):892–931, October 2002.
[76] Anna L. Paulson and Robert Townsend. Entrepreneurship and financial constraintsin thailand. Journal of Corporate Finance, 10:229–262, 2004.
[77] Thomas Piketty, Gilles Postel-Vinay, and Jean Laurent Rosenthal. Wealth concentra-tion in a developing economy. Mimeo, 2005.
[78] Thomas Piketty and Emmanuel Saez. Income inequality in the United States, 1913-1998. Quarterly Journal of Economics, 118(1):1–39, 2003.
[79] James M. Poterba. Taxation and portfolio structure: Issues and implications. InGuiso et al. [44], pages 103–142.
[80] James M. Poterba and Andrew A. Samwick. Household portfolio allocation over thelife-cycle. NBER Working Paper 6185, 1997.
[81] Vincenzo Quadrini. The importance of entrepreneurship for wealth concentration andmobility. Review of Income and Wealth, 45(1):1–19, March 1999.
[82] Vincenzo Quadrini. Entrepreneurship, saving, and social mobility. Review of EconomicDynamics, 3(1):1–40, January 2000.
[83] Vincenzo Quadrini and Jose-Victor Rıos-Rull. Models of the distribution of wealth.Federal Reserve Bank of Minneapolis Quarterly Review, 21(2):1–21, Spring 1997.
[84] Santiago Budria Rodriguez, Javier Dıaz-Gimenez, Vincenzo Quadrini, and Jose-VictorRıos-Rull. Updated facts on the U.S. distributions of earnings, income, and wealth.Federal Reserve Bank of Minneapolis Quarterly Review, 26(3):2–35, Summer 2002.
44
[85] Emmanuel Saez. Income and wealth concentration in a historical and internationalperspective. In John Quigley, editor, Poverty, the Distribution of Income, and PublicPolicy. Forthcoming.
[86] John Karl Scholz. Wealth inequality and the wealth of cohorts. mimeo, May 2003.
[87] John Karl Scholz, Ananth Seshadri, and Surachai Khitatrakun. Are Americans savingoptimally for retirement? NBER working paper 10260, January 2004.
[88] James P. Smith. Racial and ethnic differences in wealth in the health and retirementstudy. Journal of Human Resources, 30:S158–S183, 1995. Supplement.
[89] Lee Soltow. Wealth inequality in the United States in 1798 and 1860. Review ofEconomic and Statistics, 66:444–451, 1984.
[90] Kjetil Storesletten, Chris Telmer, and Amir Yaron. Asset pricing with idiosyncraticrisk and overlapping generations. Mimeo, June 1999.
[91] Steven F. Venti and David A. Wise. The cause of wealth dispersion at retirement:Choice or chance? American Economic Review, 88(2):185–191, May 1988.
[92] Kirk White. Initial conditions at emancipation: the long run effect on black-whitewealth and income inequality. Mimeo, September 2003.
[93] Edward N. Wolff. Estimates of wealth inequality in the U.S., 1962-1983. Review ofIncome and Wealth, 33:231–256, 1987.
[94] Edward N. Wolff. Changing inequality of wealth. American Economic Review,82(2):552–558, may 1992.
[95] Edward N. Wolff. International comparisons in wealth inequality. Review of Incomeand Wealth, 42:433–451, 1996.
[96] Edward N. Wolff. Recent trends in the size distribution of household wealth. Journalof Economic Perspectives, 12(3):131–150, Summer 1998.
[97] Edward N. Wolff and Marcia Marley. Long term trends in U.S. wealth inequality:Methodological issues and results. In Lipsey and Tice [69], pages 765–844.
[98] Michael Wolfson. The causes of inequality in the distribution of wealth: A simulationanalysis. Ph.D. Thesis, Cambridge University, 1977.
[99] Fang Yang. Accounting for heterogeneity in retirement wealth. Mimeo, University ofMinnesota, 2005.
[100] Fang Yang. Consumption along the life-cycle: How different is housing. FederalReserve Bank of Minneapolis Working Paper 635, May 2005.
[101] Fang Yang. How do household portfolios vary with age? Mimeo, University ofMinnesota, 2005.
[102] Rui Yao and Harold H. Zhang. Optimal consumption and portfolio choices with riskyhousing and borrowing constraints. Review of Financial Studies, 2004. Forthcoming.
45
1
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